Average Reward Dynamic Programming

Average Reward Dynamic Programming is a dynamic programming method that specifically addresses infinite-horizon Markov decision processes by optimizing the long-run average reward per time step, distinguishing it from alternative criteria such as discounted or total reward formulations.

52

Publications

2.8K

Citations

88

Authors

64

Institutions

Publications per year

2017–2026

8

Authors

88

  1. JN
    John N. Tsitsiklis

    Massachusetts Institute of Technology, United States

    4 publications · 497 citations

    4

    h-index

  2. PM
    Peter Marbach

    University of Toronto, Canada

    4 publications · 497 citations

    4

    h-index

  3. SZ
    Shlomo Zilberstein

    University of Massachusetts Amherst, United States

    3 publications · 77 citations

    3

    h-index

  4. AG
    Abhijit Gosavi

    Missouri University of Science and Technology, United States

    3 publications · 340 citations

    3

    h-index

  5. SM
    Sridhar Mahadevan

    University of Massachusetts Amherst, United States

    3 publications · 642 citations

    3

    h-index

1–5 of 88

Venues

  1. Machine Learning

    3 publications · 518 citations

    3

    h-index

  2. IEEE Transactions on Automatic Control

    3 publications · 359 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 65 citations

    2

    h-index

  4. Systems & Control Letters

    2 publications · 59 citations

    2

    h-index

  5. Mathematical Methods of Operations Research

    2 publications · 28 citations

    2

    h-index

1–5